Automated severity-based risk scoring for ice storms and freezing rain

ABSTRACT

A computer-based method for identifying ice storm risk across a geographical extent includes receiving, at a computer-based ice storm risk calculation system, historical data regarding a plurality of past ice storms. The historical data includes, for each respective one of the plurality of past ice storms, data about the size of the geographical region that was impacted by the ice storm, the thickness of ice that accumulated from the ice storm, and qualitative data (e.g., written observations in new reports, etc.) reflecting human observations of the ice storm&#39;s impact. The method further includes calculating an ice storm severity index based, in part, on the size of the geographical region that was impacted by the ice storm and the thickness of the accumulated ice that resulted from the ice storm, and validating the calculated ice storm index with the qualitative data reflecting the human observations of the ice storm&#39;s impact.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority to co-pending U.S.Provisional Patent Application No. 62/591,234, entitled AUTOMATEDSEVERITY-BASED RISK SCORING FOR ICE STORMS AND FREEZING RAIN, which wasfiled on Nov. 28, 2017, the disclosure of which is incorporated hereinby reference in its entirety.

FIELD OF THE INVENTION

This disclosure relates to ice storms and freezing rain and, moreparticularly, automated severity-based risk scoring for ice storms andfreezing rain.

BACKGROUND

Freezing rain events and their more severe counterpart, ice storms, mayoccur when precipitation falls through atmospheric layers with differingthermal profiles. When precipitation begins in a sub-freezingenvironment aloft in the atmosphere, it begins to fall as snow. If lowerlevels of the atmosphere are above freezing, this falling snow will meltinto rain droplets. When rain encounters a surface environment that isbelow freezing, the rain freezes on contact with the sub-freezingsurfaces, and it is termed freezing rain. This freezing rain can accreteon the surfaces and objects below freezing, such as trees, powerlines,and roads, potentially causing substantial damage and travel delays.Freezing rain events with damaging ice accretion are generally includedin what is referred to as ice storms in this document.

Freezing rain events and ice storms are uniquely damaging weather eventsthat impact many areas around the globe. They often result in widespreaddisruption, including loss of electricity, impassable roads, and downedtrees and power lines. Some estimates place the annual insured lossesattributable to winter storms to over $2 billion a year, and severeindividual events, like the 1998 Ice Storm affecting a larger portion ofNorth America, including major metropolitan centers in the United Statesand Canada, can top even that annual amount.

It may be desirable for affected communities, the insurance industry,and others to accurately understand and account for the level of icestorm risk at one or more particular locations.

SUMMARY OF THE INVENTION

In one aspect, a computer-based method is disclosed for identifying icestorm risk across a geographical extent (e.g., across the contiguousUnited States). The method includes receiving, at a computer-based icestorm risk calculation system, historical data regarding a plurality ofpast ice storms. The historical data includes, for each respective oneof the plurality of past ice storms, data about the size of thegeographical region that was impacted by the ice storm, the thickness ofice that accumulated from the ice storm, and qualitative data (e.g.,written observations in news reports, etc.) reflecting humanobservations of the ice storm's impact. The method further includescalculating an ice storm severity index based, in part, on the size ofthe geographical region that was impacted by the ice storm and thethickness of the accumulated ice that resulted from the ice storm, andvalidating the calculated ice storm index with the qualitative datareflecting the human observations of the ice storm's impact.

In another aspect, a computer system is disclosed that includes aplurality of data sources (e.g., the Cold Regions Research andEngineering Lab (CRREL) database of damaging ice storms 102 a, theground station Meteorological Terminal Aviation Routine (METAR) weatherreports database 102 b, and the quality-controlled daily summaries ofground station weather data from the Global Historical ClimatologyNetwork (GHCN) 102 c), a computer-based ice storm risk calculationsystem, and a plurality of computer-based user terminals (e.g., laptops,desktops, tablets, smart phones, etc.). The plurality of data sources,the computer-based ice storm risk calculation system, and the pluralityof computer-based user terminals are coupled to one another forcommunication via a network (e.g., the Internet).

The computer-based ice storm risk calculation system has acomputer-based processor, and a computer-based memory coupled to thecomputer-based processor. The computer-based processor is configured toexecute instructions stored in the computer-based memory and to performvarious steps that include receiving historical data regarding aplurality of past ice storms from a first one of the data sources (e.g.,CRREL), as well as the other data sources. The historical data fromCRREL includes, for each respective one of the plurality of past icestorms, data that indicates: a size of a geographical region that wasimpacted by the ice storm, a thickness of accumulated ice that resultedfrom the ice storm (when such data is available), and qualitative data(e.g., reports in printed publications) reflecting human observations ofthe ice storm's impact. The processor calculates an ice storm severityindex based (for each respective storm), at least in part, on the sizeof the geographical region that was impacted by the ice storm and thethickness of the accumulated ice that resulted from the ice storm. Theprocessor also validates the calculated ice storm index with thequalitative data reflecting the human observations of the ice storm'simpact.

The computer system is configured to make available at one or more ofthe computer-based user terminals a gridded geographic map with aplurality of grid cells, where each respective grid cell has acorresponding risk level that is viewable or accessible from thecomputer-based user terminal.

In some implementations, one or more of the following advantages arepresent.

In various implementations, the techniques and technologies disclosedherein provide for a relatively comprehensive risk scoring paradigmutilizing a broad risk scoring scheme that analyzes severity based inpart on multiple historical-based severity indicators. This paradigm, invarious implementations, calculates ice storm and/or freezing rain riskat one or more locations based on the historical probability of an eventcombined with other information, such as a comprehensive analysis ofrisk using multiple severity indicators. These severity indicators caninclude, for example, reported ice thickness and/or textual analysis ofnews and observer reports. The paradigm, in various implementations,also, or alternatively, calculates severity based on total freezing rainprecipitation amounts recorded, which historically may be more accuratethan other imperfect gauges of ice accretion. This robust severityscoring methodology may be coupled with frequency information to yield arisk score that can be displayed graphically to allow easyinterpretation of risks across geographic areas.

Other features and advantages will be apparent from the description anddrawings and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic representation of an exemplary computer-basednetwork that includes a highly-accurate system for identifying ice stormrisk with a high degree of granularity across a geographical extent.

FIG. 2 is a schematic representation of the network in FIG. 1 with amore detailed view of the highly-accurate system for identifying icestorm risk with a high degree of granularity across a geographicalextent.

FIG. 3 is a flowchart of a process performed by the network of FIG. 1.

FIG. 4 shows an exemplary screenshot that the system might produce ormake available at one or more of the network's computer-based userterminals.

FIG. 5 shows another exemplary screenshot that the system might produceor make available at one or more of the network's computer-based userterminals.

Like reference numerals refer to like elements.

DETAILED DESCRIPTION

FIG. 1 is schematic representation of an exemplary computer network 100configured to facilitate and perform automatic calculation ofseverity-based risk scores for ice storms and freezing rain.

The network 100 has multiple sources (102 a, 102, 102 c) of historicalweather-related data that include both quantitative and qualitativehistorical information about past ice storms and freezing rain events.The network 100 also has an exemplary system 106 for automatedseverity-based risk scoring for ice storms and freezing rain (referredto herein, in some places, as an “automated risk calculation system,” orthe like, or simply “system 106”). The network 100 also has a pluralityof computer-based user terminals 114. The sources of historicalweather-related data (102 a, 102 b, 102 c), the system 106 for automatedrisk calculation, and the user terminals are all coupled together asshown via a network 108 (e.g., the Internet).

In the illustrated implementation, and as discussed herein in detail,the sources of historical weather-related data include the Cold RegionsResearch and Engineering Lab (CRREL) database of damaging ice storms 102a, a ground station Meteorological Terminal Aviation Routine (METAR)weather reports database 102 b, and quality-controlled daily summariesof ground station weather data from the Global Historical

Climatology Network (GHCN) 102 c. Of course, in other implementations,different sources of historical weather-related data may be used.Likewise, in some implementations, more or less than three sources ofhistorical weather-related data may be user.

The system 106, in the illustrated implementation, has both processing(110) and memory storage (112) capabilities/modules. In variousimplementations, the processing module can include any number of (one ormore) processors to perform and/or facilitate the various processingfunctionalities disclosed herein as being attributable to system 106.Likewise, in various implementations, the memory storage module 112 caninclude any number of (one or more) memory storage devices configured toperform or facilitate one or more of the memory storage functionalitiesdisclosed herein as being attributable to or for the system 106. Theprocessing functionalities and/or the memory storage functionalities canbe located in one geographic place, or can be distributed geographicallyacross different locations.

The computer-based user interface devices 114 can include virtually anykind of computer-based devices including, for example, laptops, tablets,desktops, etc.

The system 100 is generally configured to download various historicaldata from the sources of historical weather-related data (102 a, 102 b,102 c), process that data, produce a geographical map divided with agrid into discrete cells, and produce a risk score for each cell thatrepresents a probability of damage occurring from either ice storms orfreezing rain in the geographical region that corresponds to theassociated cell. As discussed herein, the risk assessment in this regardis extremely robust, accurate, and granular. In a typicalimplementation, the risk assessment information that the systemproduces, and/or any raw data that the system receives, can be presentedto a system user at any one of the system's computer-based userterminals 104. As an example, the system 100, in this regard, mightpresent at one of the system's computer-based user terminals, a visualrepresentation of a gridded geographical map with corresponding riskassessment information (e.g., risk scores) associated with each cell inthe grid. In various implementations, the user may be able to interfacewith and/or access system information from one or more of thecomputer-based user terminals 104 in a number of different ways, some ofwhich are described herein.

FIG. 2 is a partial schematic diagram of the network 100 in FIG. 1showing a detailed view of an exemplary configuration for the automatedrisk calculation system 106.

The automated risk calculation system 106 in the illustratedimplementation is connected, at its input side, to the three sources ofhistorical weather-related data in FIG. 1: the Cold Regions Research andEngineering Lab (CRREL) database of damaging ice storms 102 a, a groundstation Meteorological Terminal Aviation Routine (METAR) weather reportsdatabase 102 b, and quality-controlled daily summaries of ground stationweather data from the Global Historical Climatology Network (GHCN) 102c. The automated risk calculation system 106 in the illustratedimplementation is connected, at its output side, to one or morecomputer-based user terminals (see “to 114”).

The internal components of an automated risk calculation system 106 andthe specific configuration of those internal components can vary. FIG. 2shows one such example thereof. In some implementations, one or more (orall) of the components in the illustrated automated risk calculationsystem 106 can be implemented as one or more discrete physicalcomponents. In some implementations, one or more (or all) of thecomponents in the exemplary automated risk calculation system 106, andtheir respective functionalities, can be implemented by the system's 106processor 110 executing software instructions stored on the system'scomputer-based memory storage 112 (or elsewhere), and any associatedmemory storage functionalities may be supported (or performed) by thecomputer-based memory storage 112 (or other memory) as well.

At a high level, the automated risk calculation system 106, includingthe exemplary components and configuration represented in FIG. 2, isoperable to (and does) perform and/or facilitate performance of thevarious processing functionalities disclosed herein as attributable tothe automated risk calculation system 106.

The illustrated automated risk calculation system 106 is conceptuallyorganized into a CRREL ingesting and pre-processing module 216, a CRRELprobability estimator 218, a CRREL severity-weighted risk indexcalculator 220, a METAR/GHCN ingesting and pre-processing module 222, aMETAR/GHCN probability estimator 224, a METAR/GHCN severity-weightedindex calculator 226, a risk index blender 228, a spatial smoother 230,and a data distribution/output module 232.

The CRREL ingestion and pre-processing module 216 has a CRREL datacollector/scraper 216 a, a geographical area calculator 216 b, a CRRELseverity index calculator 216 c, a parser 216 d, and a CRREL severityindex validator 216 e.

The CRREL probability estimator 218 has a CRREL historical occurrencecounter 218 a, and a CRREL probability calculator 218 b.

The METAR/GHCN ingestion and pre-processing module 222 has a METAR/GHCNdata collector/scraper 222 a, a METAR precipitation calculator 222 b, aGHCN filter and extractor 222 c, and a map-to-grid/best estimatecalculator 222 d.

The METAR/GHCN probability estimator 224 has a METAR/GHCN historicaloccurrence counter 224 a, and a METAR/GHCN probability calculator 224 b.

The automated risk calculation system 106 also has a grid generator 234that can generate a grid and/or superimposing that grid onto ageographical map.

The internal components in an automated risk calculation system 106 canbe coupled together (e.g., to facilitate communications therebetween) ina variety of different possible ways. One such way is represented in theimplementation shown in FIG. 2.

According to the illustrated implementation, the CRREL ingesting andpre-processing module 216 is connected to the CRREL probabilityestimator 218, and the CRREL ingestion and pre-processing module 216 andthe CRREL probability estimator 218 are connected to the CRRELseverity-weighted risk index calculator 220. Moreover, the METAR/GHCNingesting and pre-processing module 222 is connected to the METAR/GHCNprobability estimator 224, and the METAR/GHCN ingesting andpre-processing module 222 and the METAR/GHCN probability estimator 224are connected to the METAR/GHCN severity-weighted index calculator 226.The CRREL severity-weighted risk index calculator 220 and the METAR/GHCNseverity-weighted index calculator 226 are connected to the risk indexblender 228. The risk index blender 228 is connected to the spatialsmoother 230. The spatial smoother 230 is connected to a datadistribution/output module 232. The grid generator 234 is connected tothe CRREL ingesting and pre-processing module 216 and to the METAR/GHCNingesting and pre-processing module 222.

Within the CRREL ingesting and pre-processing module 216, the CRREL datacollector/scraper 216 a is connected to the geographical area calculator216 b and to the parser 216 d, the geographical area calculator 216 b isalso connected to the CRREL severity index calculator 216 c, the CRRELseverity index calculator 216 c is also connected to the CRREL severityindex validator 216 e, and the parser 216 d is connected to the CREELdata collector/scraper 216 a and to the CRREL severity index validator216 e.

Within the CRREL probability estimator 218, the CRREL historicaloccurrence counter 218 a is connected to the CRREL probabilitycalculator 218 b.

Within the METAR/GHCN ingesting and preprocessing module 222, theMETAR/GHCN collector/scraper 222 a is connected to the METARprecipitation calculator 222 b and to the GHCN filter and extractor 222c, and the METAR precipitation calculator 222 b and the GHCN filter andextractor 222 c are connected to the map-to-grid/best estimatecalculator 222 d.

Within the METAR/GHCN probability estimator 218, the METAR/GHCNhistorical occurrence counter 218 a is connected to the METAR/GHCNprobability calculator 218 b.

FIG. 3 is a flowchart showing an exemplary implementation of a processfor automatically calculating severity-based risk scores for ice stormsand freezing rain. The process represented in the illustrated flowchartmay be implemented, for example, by the system 100, and components, inFIGS. 1 and 2.

According to the illustrated flowchart, the process (at 350) includesdefining a grid that has a plurality of cells.

In a typical implementation, the system 106 (e.g., grid generator 234)superimposes the grid on a geographical map so that the grid and, moreparticularly the cells of the grid, will define discrete geographicalregions represented by the map. The system 106 may assign an individualrisk score to each respective one of the grid cells based on acalculated risk that damaging ice storms or freezing rain events mightoccur, or cause damage, in the corresponding geographical region. In atypical implementation, the system makes this informationviewable/accessible from one or more computer-based user terminals ofthe system 106. More particularly, in this regard, the system 106 maypresent or make available for viewing, at the screen of one of thesystem's computer-based user terminals, a map with the superimposed gridand a score in, or associated with, each respective cell in the grid.

The process of defining, or generating, the grid can be done in a numberof different ways. Typically, this process includes defining the variousproperties of the grid (e.g., cell size, cell shape, etc.) that will beproduced and superimposed over the geographical map.

The grid properties that may be specified during this process includeone or more (or all) of the following: type of grid (e.g., geodesic,etc.), where (over the map) the grid should appear, how the grid shouldbe aligned with the map, grid resolution (e.g., cells per unit of area),cell size, cell shape, etc.

These properties may be defined automatically (e.g., based onpreprogrammed instructions being executed by the system 106), ormanually (e.g., in response to a user manually entering values for theproperties in response to user prompts that appear at one of thesystem's computer-based user terminals), or by some combination ofautomatic and manual processes. If any of the grid properties are to beentered manually, then the system 106 may be present to the user aseries of prompts, each referring to a different property of the gridbeing defined.

Defining the grid enables the system 106 to present precise risk data ina grid-based environment that can provide a high degree of granularity.This high degree of granularity can, if sufficiently granular, beparticularly advantageous over city-wide or region-wide riskscoring/event prediction schemes. Moreover, grid-based scoring can helpusers easily and quickly digest risk information by providing the riskinformation in an intuitive, easy-to-use, and easy-to-understandgraphical format. This sort of graphical, grid-based format also makesit easy to discern patterns in the risk information that otherwise mightbe difficult to recognize or appreciate.

In an exemplary implementation, the grid is geodesic with hexagonalcells. In general terms, a geodesic grid uses triangular tiles based onthe subdivision of a polyhedron and can be used to subdivide the surfaceof the Earth (or some portion of the Earth, e.g., the United States)represented on a map into cells. This kind of grid generally does nothave a straightforward relationship with latitude and longitude.Typically, the size and shape of each cell in a geodesic grid isidentical even near the poles where many other spatial grids might havesingularities or heavy distortions.

In one exemplary implementation, the system's 100 grid generatorperforms or at least facilitates the grid defining process. In thisregard, the grid generator may utilize or leverage one or more functionsfrom the SPHEREPACK™ open-source software library. The SPHEREPACK™open-source software library is a collection of FORTRAN77™ programs andsubroutines that can be used to facilitate computer modeling of variousprocesses. In this regard, the SPHEREPACK™ functionalities can be usedto locate centroids of the geodesic cells relative to the map, and toperform other associated functionalities including, for example,generating hexagonal boundaries of the geodesic cells by performing thegeometrical calculations to define the boundaries of the hexagons inlatitude and longitude coordinates, and matching the resolution andorientation of the hexagons with the cell centroids.

The grid can end up having any one of a variety of differentconfigurations. For example, the grid need not be geodesic; the grid canhave virtually any other convenient configuration, with cells ofvirtually any size and shape. Moreover, the size and shape of the cellsneed not be uniform throughout the grid. In some implementations, thecells can have a size and shape that corresponds to certain latitudinaland longitudinal lines, or that corresponds to geographical borders ofcertain cities or towns, or other geographical or topographicalfeatures. The grid can have virtually any kind of resolution (e.g., from1 kilometers to 30 kilometers or more).

In one exemplary implementation, the grid will cover the entirecontiguous United States and has a 15 kilometer resolution (i.e., thedistance between the centroids of any two neighboring cells is 15kilometers).

In the next part of the process (at 304), the system 106 ingests datathat is relevant to the risk of damaging ice storms and/or freezing rainevents for use in creating the cell-specific risk scores. The data thatthe system 106 ingests in this regard can include a variety of differentdata types and can come from a variety of different sources typicallyoutside of the system 106. Generally speaking, the ingested data ishistorical in nature (i.e., the data relates to ice storms and freezingrain events that have happened in the past). Moreover, in a typicalimplementation, the ingested data includes at least some quantitativedata and at least some qualitative data about the ice storms andfreezing rain events of interest. The quantitative information caninclude, for example, start dates, end dates, shape information, sizeinformation, latitudes, longitudes, ice thicknesses, hour-by-hourprecipitation estimates, etc. The qualitative information can includepublished articles, or other reliable qualitative reports, and/orexcerpts thereof that describe the past ice storms or freezing rainevents of interest. In a typical implementation, the system 106leverages both the quantitative data and the qualitative information toproduce a robust, highly accurate, and highly granular risk scores fordifferent cells across a particular geographical extent.

According to the illustrated implementation, the system 106 ingeststhree data sets from:

-   -   1. The Cold Regions Research and Engineering Lab (CRREL)        database of damaging ice storms (at 304 a),    -   2. Ground station Meteorological Terminal Aviation Routine        (METAR) weather reports (at 304 b), and    -   3. Quality-controlled daily summaries of ground station weather        data from the Global Historical Climatology Network (GHCN) (also        at 304 b).

CRREL is a United States Army Corps of Engineers, Engineer Research andDevelopment Center research facility headquartered in Hanover, N.H.CRREL provides scientific and engineering support to the U.S.government, its military, and others with a core emphasis on informationabout cold environments. CRREL maintains a database that includesinformation, including quantitative information, about damaging icestorms. That database is accessible through CRREL's Damaging Ice StormGeographic Information System (“GIS”). CRREL also maintains webpageswith HTML (“hypertext markup language”) descriptions of publishedarticles (e.g., newspaper articles, magazine articles, etc.) thatdescribe the past ice storms or freezing rain events of interest. Thesearticles may include some quantitative information (e.g., icethicknesses, storm locations, amounts of precipitation, etc.) about theice storms and freezing rain events, but also may contain qualitative(or non-quantitative) information about the ice storms and freezing rainevents and their potentials for causing property damage or destruction(e.g., describing certain storms as the “worst,” or “most destructive,”or “most harmful” storm of the year, etc., referred to herein as “worst”descriptors). In a typical implementation, the system 106 (e.g., theCRREL ingesting and pre-processing module 216, at 304 a) would ingestand preprocess both quantitative and qualitative data from CRREL.

More particularly, according to the illustrated implementation, thesystem 106 (at 304 a) ingests and preprocesses data from CRREL by:downloading (mostly quantitative) GIS data and scraping (mostlyqualitative) HTML storm descriptions from CRREL (at 304 a 1),calculating spatial areas for each storm (at 304 a 2), parsing the HTMLfor ice thicknesses and “worst” descriptors (at 304 a 3), and creatingindices from area information and ice thicknesses and validating thoseindices against (qualitative) descriptors (at 304 a 4).

In a typical implementation, (at 304 a 1) the GIS data may be downloaded(e.g., by the CRREL collector scraper) from CRREL's GIS database via awireless network (e.g., the Internet). The GIS data can be downloadedmanually or automatically (e.g., at set times, whenever new GIS databecomes available at CRREL's GIS database, in response to a prompt froma system user or component, etc.).

The downloaded GIS data can have any one of a variety of differentformats or combinations of formats and/or data types. For example, in anexemplary implementation, at least some of the downloaded information isin a shapefile format. The shapefile format is a popular geospatialvector data format for GIS systems to encode geographical informationinto a computer file. The shapefile format can spatially describe vectorfeatures: points, lines, and polygons, representing items such as waterwells, rivers, lakes, storms, etc. Each item may have one or moreattributes, represented in the shapefile, that describe the item, suchas name, temperature, ice thickness, etc. In some implementations, thedownloaded GIS data can include quantitative data, such asidentification numbers for the ice storms, start and end dates for theice storms, information about the geographical extent/size of the icestorms (e.g., number of acres and the perimeter surrounding the areaaffected for each ice storm), etc.

Table 1, below, shows an example of data that may be downloaded from theCRREL GIS by system 106.

TABLE 1 Raw CRREL data prior to metadata transforms and severityindicator creation ID START_DATE END_DATE SOURCETHM AREA PERIMETER ACRES339 Jan. 11, 1956 Jan. 19, 1956 Fp09_all.shp 32195.898 706.719321962793.6 340 Mar. 7, 1956 Mar. 11, 1956 Fp09_all.shp 6160.254 337.3661603381.39 346 Dec. 6, 1958 Dec. 18, 1958 Fp09_all.shp 2198.356 198.09521983573.15 346 Dec. 6, 1958 Dec. 18, 1958 Fp09_all.shp 1848.129 157.03618481346.42 347 Jan. 12, 1982 Jan. 20, 1982 Fp09_all.shp 237295.0713617.943 2372964091 347 Jan. 12, 1982 Jan. 20, 1982 Fp09_all.shp1709.565 233.26 17095468.69 349 Feb. 8, 1959 Feb. 13, 1959 Fp09_all.shp81335.075 2210.137 813345774.4 351 Dec. 15, 1982 Jan. 3, 1982Fp09_all.shp 3250.198 209.183 32501941.11 352 Dec. 11, 1959 Dec. 15,1959 Fp09_all.shp 22152.093 534.441 221521633.7 353 Dec. 23, 1959 Jan.6, 1960 Fp09_all.shp 4681.588 293.862 46815730.97 361 Mar. 17, 1984 Mar.21, 1984 Fp09_all.shp 129135.661 1572.606 1291362622 364 Mar. 2, 1985Mar. 7, 1985 Fp09_all.shp 8761.616 533.28 87615618.39 364 Mar. 2, 1985Mar. 7, 1985 Fp09_all.shp 140889.647 3124.441 1408858004

This exemplary table includes data that relates to ten different icestorms. Each ice storm is identified by its own unique, CRREL-assigned,identification number: 339, 340, 346, 347, 349, 351, 352, 353, 361, and364. The data in the table includes, for each represented storm: theunique, CRREL-assigned, identification number (in the ID column), astart date (in the START DATE column), an end date (in the END DATEcolumn), a shapefile (in the SOURCETHM column) that contains thegeographical shape of the storm, and other information about thegeographical extent of the storm's impact (in the AREA column, thePERIMETER column, and the ACRES column, which may be derived frominformation in the shapefile).

For most of these storms (e.g., ID numbers 339, 340, 349, 351, 352, 353,and 361) there is only one line of data in the table, but for some(e.g., ID numbers 346, 347, and 364) there are two lines of data in thetable. For the storms that have more than one line of data, each linemay relate to a different geographical region that was affected by thestorm. For example, the first line of data for storm 346 may relate tothat storm's activity in a first geographical region, and the secondline of data for storm 346 may relate to that storm's activity in asecond geographical region that is different than the first geographicalregion. The second geographical region may be adjacent to the firstgeographical region.

In some implementations, for the storms that have more than one dataentry (like storms 346, 347, and 364) in a data collection such as theone in Table 1, the system 106, when it receives such data, will combinethe multiple entries into one. The system 106 may do this, at least inpart, by superimposing the geographical shape of the storm shown in bothof the corresponding shapefiles and then calculating a new area,perimeter, and/or acres based on the resulting new (combination) stormshape.

Referring again to FIG. 3, the process (still at 304 a 1) includesscraping storm description data from the HTML webpages maintained byCRREL that describe past ice storms or freezing rain events. The datascraped from these HTML webpages can be quantitative in nature (e.g.,values for ice thickness), qualitative in nature (e.g., statementscharacterizing certain storms as the “worst,” “most destructive,” “mostharmful” of the year, etc.), or a combination of quantitative andqualitative data. The scraping can be manual (e.g., by a user siftingthrough articles/publications at CRREL's website from one of thesystem's computer-based user terminals) or automatic (e.g., by acomputer-based web scraper being executed by the system 106). Acomputer-based web scraper can be implemented as an applicationprogramming interface (API) that gathers and copies data, typically intoa central local database or spreadsheet (maintained by the system 106),for later retrieval and/or analysis.

In a typical implementation, the data gathered or copied by the webscraper includes entire articles/publications/reports that are availableat CRREL's website. These articles/publications/reports typically areorganized into a nested, indexed (by CRREL), collections of linkedwebpages in HTML format. In some implementations, the web scraper actsas a web crawler and navigates through the nested, indexed, collectionsof linked webpages in a systematic manner, and fetches thearticle/publication/report at each webpage automatically (withoutongoing user involvement) for later processing and/or data extraction.

In some implementations, of course, the web scraping process can be morelimited and nuanced. In those implementations, the web scraper may beconfigured to fetch (download) only certain types of information fromeach web page it accesses. In this regard, the web scraper may beconfigured to search for (and copy only) information from the webpagesthat is relevant to determine the severity, destructive capacity, and/orharmfulness of each associated storm. This information can include, forexample, storm names, storm locations, ice thickness amounts, “worst”descriptors, whether trees were downed, whether electrical power wasinterrupted, etc. In implementations where the web scraper performs thismore limited and nuanced fetching protocol, the web scraper may access apreprogrammed list of search terms and phrases to find storm names,storm locations, ice thickness amounts, “worst” descriptors, whethertrees were downed, whether electrical power was interrupted, etc. ateach webpage it access.

Next (at 304 a 2), according to the illustrated implementation, thesystem 106 (e.g., area calculator 216 b) calculates a spatial area foreach storm. In a typical implementation, these calculations areperformed based on the geospatial data obtained from CRREL (e.g., in theshapefile, which identifies the geographical region that was impacted bythe corresponding storm). This step may be redundant, particularly ifthe data downloaded from CRREL included a value of spatial area for eachstorm (as shown above in Table 1). However, performing a spatial areacalculation (at 304 a 2) independent of any spatial area calculationsperformed by CRREL or spatial area data provided by CRREL can be used asa double check or confirmation of the CRREL-provided data. In manyinstances, the spatial area calculation performed by the system 106 (at304 a 2) will yield a result that is identical to, or at least veryclose to the spatial area data provided by CRREL.

Of course, in various implementations, any subsequent steps orcalculations that the system 106 performs that may require a spatialarea value for the storm can utilize the data obtained from CRREL.Therefore, in some implementations, step 304 a 2 in FIG. 3 can bedispensed with entirely.

In a typical implementation, any data obtained from CRREL, or derivedfrom the data obtained from CRREL, is stored by the system 106 incomputer-based memory storage (e.g., in a database or the like).

Next (at 304 a 3), according to the illustrated implementation, thesystem 106 (e.g., parser 216 d) parses the HTML documents that weredownloaded/scraped from CRREL's webpages for information that may berelevant to the severity and/or destructive nature and capacity of eachassociated storm. In some implementations, this parsing process mayinclude searching each respective document for specific words or phrasessuch as specific storm names, names of locations, ice thickness amounts(e.g., including words or abbreviations for “inches,” “centimeters,”etc., words like “worst,” “terrible,” “destructive,” etc., and/or wordsor phrases that suggest that trees may have been downed, that electricalpower may have been interrupted, etc.

In a typical implementation, the searching or parsing may be conductedby a computer-based parser that includes one or more computer-basedprocessors executing software that supports and facilitates thesearching functionalities. Moreover, the specific words and/or phrasesthat the parser searches for may be preprogrammed into computer-basedmemory or may be customizable by a user in computer-based memory.

In some implementations, the textual words and/or phrases that getidentified by the parsing process are saved into computer-based memoryfor later reference.

Next (at 304 a 4), according to the illustrated implementation, thesystem 106 (e.g., severity index calculator 216 c) calculates a severityindex for each storm and validates (with validator 216 e) that severityindex against the textual words and/or phrases that were parsed (at 304a 3) from the HTML articles/publications/reports.

These severity risk indices can be calculated in any one of a variety ofdifferent ways. At a high level, each severity index is intended torepresent, or quantify, the severity, and by extension the potential fordamage, of each respective ice storm or freezing rain event representedby the data obtained from CRREL

In an exemplary implementation, the system 106 calculates a severityindex for each storm or freezing rain event (collectively referred to as“storm” herein) as a function of ice thickness for the storm, andgeographical area affected by the storm. Even more particularly, in oneexemplary implementation, the system 106 calculates a severity index foreach storm using the following formula:

I i I ^ + A i ,

where:

-   -   I_(i) is an ice thickness (e.g., in inches) for the storm (that        can be based on information that was parsed from the textual        storm descriptions),    -   Î is an average ice thickness (e.g., in inches) for all of the        storms in the database (e.g., that can be based on information        that was parsed from the textual storm descriptions),    -   A_(i) is a spatial area of the storm (e.g., a footprint or        geographical area affected by the storm, e.g. in square miles,        acres, square kilometers, etc.) that may have been downloaded        with or calculated from information from the CRREL GIS data, and    -   is an average over all the values of the square roots of the        spatial areas for all the storms in the database. In the        formula, the square root values are calculated first, 1 by 1 for        each storm in the database, then the average of all those square        root values is calculated.

There are a variety of different ways that the system 106 (at 304 a 4)might validate a calculated severity index against the textual wordsand/or phrases that were parsed from the HTMLarticles/publications/reports. At a high level, the system 106, in thisregard, utilizes this subjective severity information from the stormdescription text mining to cross-check the validity of the index valuesfor quantifying the severity of the storm. In various implementations,this validation process can identify instances of inconsistency (glaringor otherwise) between a storm's calculated severity index and thetextual words and/or phrases parsed out of HTMLarticles/publications/reports for that storm.

In other embodiments, the results of text mining, which could yieldotherwise unavailable damage reports, would themselves be included theformula for calculating severity. For instance, one embodiment may addto the severity index a factor equal to the ratio of the number ofpeople losing electricity to the average of number of people losingelectricity across the collected set of storms. Another embodiment couldsimilarly add a factor to the index based on the ratio of social mediamentions or reports to the average mentions or reports for the collectedstorms. Other techniques are possible as well.

According to an exemplary implementation, the system 106 performsregression testing between each severity index value and textualinformation that was parsed from the HTML articles/publications/reports.For example, the system 106 may be configured to count the number ofarticles about a particular storm that include the word “worst,” andthen check to make sure that the calculated severity index is notinconsistent (or at least not glaringly so) with that count. If thesystem 106 in this example calculates a low severity index for aparticular storm (suggesting that the storm was not very severe at all),but the parsing process identifies three different articles about thestorm that include the word “worst” (e.g., as in “worst storm of theyear,” etc.), then the validation process might flag that particularseverity index as being suspect/not necessarily reliable.

In those instances, where the validation process flags a suspectseverity index, the system 106 may: 1) discard the suspect severityindex, and/or 2) send a message (via email, text, push notification,website notification, etc.) to a system user that flags the severityindex as suspect. The system user, then, may take steps to addressand/or correct for any shortcoming or problems associated with thesuspect severity index, including possibly modifying the formula used tocalculate the severity index. The system 106 may be configured toprovide an interface (at one or more of the computer-based userterminals) that a user can use to modify the severity index formula.

After the system 106 calculates (and, optionally, validates) the icestorm severity indices, the system 106 stores them, typically in atemporary storage medium of some form, such as RAM, for later use.

Table 2, below, shows an example of system data after the CRREL data isingested, processed, and after the severity indices are calculated.

TABLE 2 System data after the CRREL data has been ingested, processed,and after severity indices have been calculated Storm Ice ThicknessWorst Ice Storm ID Date Area (Inches) Count Index 339 Jan. 11, 195632196 0.75 0 1.41 340 Mar. 7, 1956 6160 0 1.22 346 Dec. 6, 1958 40460.50 0 1.61 347 Jan. 12, 1982 239005 2 1.70 349 Feb. 8, 1959 81335 0.631 1.77 351 Dec. 15, 1982 3250 0.50 0 1.06 352 Dec. 11, 1959 22152 0.75 01.47 353 Dec. 23, 1959 4682 0.92 3 2.30 361 Mar. 17, 1984 129136 2.00 01.19 364 Mar. 2, 1985 149651 0.42 0 2.04

This exemplary table includes data that relates to ten different icestorms: Storm IDs 339, 340, 346, 347, 349, 351, 352, 353, 361, and 364.The data in the table includes, for each represented storm: the unique,CRREL-assigned, identification number (in the STORM ID column), a datethat corresponds to CRREL's recorded start (in the DATE column), an areaof the storm (in the AREA column) that may have been downloaded from theCRREL database or calculated based on the downloaded CRREL data), an icethickness (in the ICE THICKNESS (INCHES) column, a count of the numberof articles or number of times in articles that the word “worst”appeared for each storm (in the WORST COUNT column), and the calculatedice storm index for each storm (in the ICE STORM INDEX column).

In Table 1, the storm with the lowest severity index is Storm ID No.351, with a severity index of 1.06 and a worst count of 0. The stormwith the highest severity index is Storm ID No. 353, with a severityindex of 2.30 and a worst count of 3. These two exemplary table entriesshow a high correlation between the calculated severity indices and thetextual information (worst counts) parsed from the HTMLarticles/publications/reports. It is also worth noting that the CRRELdata for storm ID No. 346 in Table 1 had two entries, but the system 106consolidated those two entries into just one entry in Table 2.

Next (at 304 b), the process has the system 106 (e.g., the METAR/GHCNingesting and pre-processing module 222) ingesting and pre-processing ofMETAR and GHCN station data.

METAR is basically a format for reporting weather information. METARweather reports may be used by pilots and/or meteorologists to generallyassist in weather forecasting. Raw METAR is a very common format in theworld for the transmission of observational weather data. It is highlystandardized, which allows it to be understood throughout most of theworld. METARs typically come from airports or permanent weatherobservation stations. Reports tend to be generated according to apredefined schedule (e.g., once an hour or half-hour), but if conditionschange significantly, special reports may be issued. Some METARs arecreated by automated weather stations, some use augmented observations,which are recorded by digital sensors, encoded via software, and thenreviewed by certified weather observers or forecasters prior to beingtransmitted, and some use observations by trained observers orforecasters who manually observe and encode their observations prior totransmission.

The GHCN is essentially an integrated database of climate summaries fromland surface stations across the globe that are been subjected to acommon suite of quality assurance reviews. The database includes datacollected from many continuously reporting fixed stations at the Earth'ssurface and represents the input of approximately 6000 temperaturestations, 7500 precipitation stations, and 2000 pressure stations. Someof the data is more than 175 years old and some of the data may be lessthan an hour old.

According to the illustrated implementation, ingesting andpre-processing of METAR and GHCN station data involves the system 106pulling raw data from these sources (at 304 b 1), calculating dailytotal precipitation amounts and daily freezing precipitation amountsfrom the METAR data (at 304 b 2), filtering the GHCN data for freezingrain days and extracting daily precipitation totals (at 304 b 3), andmapping the data to the grid and getting a “best” estimate for dailyfreezing rain (at 304 b 4).

The pulling of data from METAR (by the METAR/GHCN collector/scraper 222a, at 304 b 1) typically includes downloading the data, formatting thedata and storing it in computer-based memory (e.g., in a relationaldatabase). Table 3, below, shows an example of what the data in thisregard would look like after it has been downloaded to the system 106,formatted by the system 106 and stored in a PostgreSQL database.

TABLE 3 METAR data as stored in a PostgreSQL database SensorId LatitudeLongitude DateTimeGmt PrecipPrevHr_mm PresentWeather KPWM 43.650002−70.32 1/19/11 0:51 0.51 Light FZRA BR KPWM 43.650002 −70.32 1/19/111:51 0.76 Light FZRA BR KPWM 43.650002 −70.32 1/19/11 2:51 0.76 LightFZRA BR KPWM 43.650002 −70.32 1/19/11 3:51 0.25 Light FZRA BR KPWM43.650002 −70.32 1/19/11 4:51 0.25 Light FZRA BR KPWM 43.650002 −70.321/19/11 5:20 0 N/A KPWM 43.650002 −70.32 1/19/11 5:51 0 N/A KPWM43.650002 −70.32 1/19/11 6:03 0 Light FZRA KPWM 43.650002 −70.32 1/19/116:31 0 Moderate UP KPWM 43.650002 −70.32 1/19/11 6:51 0 N/A KPWM43.650002 −70.32 1/19/11 7:51 9999 N/A KPWM 43.650002 −70.32 1/19/118:51 0.25 Light SN BR KPWM 43.650002 −70.32 1/19/11 9:15 0.51 Light SNBR KPWM 43.650002 −70.32 1/19/11 9:33 0.51 Moderate UP BR KPWM 43.650002−70.32 1/19/11 9:51 0.51 Light RA BR KPWM 43.650002 −70.32 1/19/11 10:510 Light RA BR KPWM 43.650002 −70.32 1/19/11 11:51 0.25 Light RA KPWM43.650002 −70.32 1/19/11 12:51 0.25 Light RA KPWM 43.650002 −70.321/19/11 13:51 0 N/A KPWM 43.650002 −70.32 1/19/11 14:51 9999 N/A KPWM43.650002 −70.32 1/19/11 15:51 9999 N/A KPWM 43.650002 −70.32 1/19/1116:51 9999 Moderate BR KPWM 43.650002 −70.32 1/19/11 17:51 0.51 Light RAKPWM 43.650002 −70.32 1/19/11 18:51 0.51 Light RA KPWM 43.650002 −70.321/19/11 19:51 0.76 Light SN BR KPWM 43.650002 −70.32 1/19/11 20:51 0.51Light SN BR KPWM 43.650002 −70.32 1/19/11 21:51 0.51 Light SN BR KPWM43.650002 −70.32 1/19/11 22:19 0 Light SN BR KPWM 43.650002 −70.321/19/11 22:51 0 Light SN BR KPWM 43.650002 −70.32 1/19/11 23:46 0.25Light SN BR KPWM 43.650002 −70.32 1/19/11 23:51 0.25 Light SN BR

This exemplary table includes precipitation and weather data over thecourse of a day collected periodically (e.g., hourly and then a fewother readings) at a particular sensor location. The data in the tableincludes, for each time entry: a sensor identifier (in the SENSORIDcolumn), geolocation data for the sensor (in the LATITUDE and LONGITUDEcolumns), date and time information, in Greenwich Mean Time (in theDATETIMEGMT column), previous hour precipitation amounts, in millimeters(in the PRECIPPREVHR_MM column), and information about then-currentweather conditions (in the PRESENTWEATHER column).

The sensor ID, in the illustrated example, is KPWM, which is anInternational Civil Aviation Organization (ICAO) designator that refersto a weather sensor at the Portland International Jetport in CumberlandMe., United States. The abbreviations in the PRESENTWEATHER column areas follows: FZ=Freezing, RA=Rain, BR=Mist, UP=Unknown Precipitation, andSN=Snow.

The pulling of data from GHCN (at 304 b 1) may involve the system 106downloading the data, formatting the data and storing it incomputer-based memory (e.g., in a relational database or any other typeof computer-based memory).

Table 4, below, shows an exemplary collection of raw GHCN data that thesystem 106 may have downloaded.

TABLE 4 Raw GHCN Data USW00014745201101TMAX 106 X 61 X 17 X 11 X 11 X−11 X −22 X −11 X 11 X −11 X −11 X −22 X −22 X −61 X −44 X −11 X −89 X 0X 22 X −22 X −28 X −50 X −78 X −144 X −83 X −28 X −6 X 11 X 11 X −17 X−72 X USW00014745201101TMIN −39 X 6 X −94 X−128 X −122 X −150 X −150 X−56 X −56 X −78 X −150 X −56 X −111 X −156 X −233 X −117 X −206 X −183 X−33 X −89 X −117 X −217 X −206 X −239 X −200 X −211 X −139 X −172 X −100X −172 X −178 X USW00014745201101PRCP 0T X 10 X 0 X 0 X 0T X 0 X 0T X 3X 0 X 0 X 0 X 328 Z 0 X 0 X 3 X 0 X 0 X 231 X 66 X 0T X 81 X 0 X 0T X 0X 13 X 10 X 48 X 0T X 5 Z 0 X 0 X USW00014745201101SNOW 0 X 0 X 0 X 0 X0T X 0 X 0T X 13 Z 0 X 0 X 0 X 465 X 0 X 0 X 18 X 0 X 0 X 175 X 36 Z 0TX 122 X 0 X 0T X 0 X 43 Z 10 Z 46 Z 0T X 20 X 0 X 0 XUSW00014745201101SNWD 127 Z 76 Z 51 Z 51 Z 51 Z 51 Z 51 Z 51 Z 51 Z 51 Z51 Z 178 Z 483 Z 432 Z 406 Z 381 Z 356 Z 330 Z 432 Z 432 Z 533 Z 508 Z508 Z 483 Z 559 Z 559 X 559 Z 533 Z 533 Z 533 Z 508 ZUSW00014745201101AWND 0 W 10 W 55 W 1 W 24 W 5 W 17 W 24 W 61 W 69 W 8 W63 W 55 W 24 W 13 W 35 W 18 W 21 W 25 W 22 W 33 W 10 W 36 W 38 W 3 W 12W 39 W 4 W 2 W 13 W 34 W USW00014745201101FMTM 2208 X 2345 X 0428 X 1344X 1805 X 1114 X 1432 X 2107 X 1307 X 1306 X 0041 X 0608 X 1323 X 1635 X1643 X 1329 X 1147 X 2323 X 1856 X 1356 X 1531 X 1241 X 2127 X 0307 X0846 X 2004 X 1200 X 1605 X 1507 X 1809 X 1126 X USW00014745201101PGTM1939 W 2351 W 0427 W 1408 W 1804 W 1850 W 1446 W 2246 W 1347 W 0406 W0007 W 0623 W 1146 W 1634 W 1637 W 1024 W 1146 W 2101 W 1855 W 1416 W1525 W 1147 W 2126 W 0223 W 1254 W 1934 W 1225 W 1536 W 1507 W 1808 W1108 W USW00014745201101WDF2 260 X 300 X 320 X 160 X 300 X 330 X 80 X330 X 320 X 310 X 320 X 320 Z 300 X 320 X 160 X 300 X 310 X 310 X 320 X300 X 300 X 330 X 320 X 310 X 310 X 30 X 320 X 180 X 300 X 300 X 310 XUSW00014745201101WDF5 60 X 300 X 310 X 30 X 290 X 20 X 50 X 320 X 330 X290 X 320 X 340 Z 300 X 320 X 180 X 300 X 310 X 30 X 310 X 280 X 290 X320 X 320 X 300 X 320 X 40 X 320 X 170 X 300 X 300 X 300 XUSW00014745201101WSF2 22 X 89 X 130 X 22 X 89 X 45 X 45 X 54 X 103 X 107X 58 X 98 Z 107 X 58 X 63 X 89 X 63 X 54 X 58 X 72 X 112 X 67 X 103 X 80X 27 X 58 X 125 X 45 X 54 X 67 X 76 X USW00014745201101WSF5 27 X 130 X183 X 31 X 112 X 94 X 58 X 63 X 139 X 148 X 76 X 134 Z 143 X 80 X 80 X116 X 76 X 67 X 72 X 89 X 156 X 89 X 130 X 116 X 27 X 72 X 148 X 63 X 58X 94 X 107 X USW00014745201101WT01 1 W 1 X−9999 −9999 1 W−9999 −9999 1W−9999 −9999 −9999 1 W−9999 −9999 1W 1 W−9999 1 W 1 W 1 W 1 W−9999 1W−9999 1 W 1 W 1 W 1 W 1 W 1 W−9999 USW00014745201101WT02−9999 1 X−9999−9999 −9999 −9999 −9999 −9999 −9999 −9999 −9999 1 X−9999 −9999 −9999−9999 −9999 1 X−9999 −9999 1 X−9999 −9999 −9999 −9999 −9999 −9999 −99991 X 1 X−9999 USW00014745201101WT08 1 X−9999 −9999 −9999 1 W−9999 −9999 1W−9999 −9999 −9999 1 X−9999 −9999 −9999 −9999 −9999 −9999 −9999 1 X 1W−9999 1 W−9999 1X 1X 1W 1X 1 X−9999 −9999 USW00014745201101WT09−9999−9999 −9999 −9999 −9999 −9999 −9999 −9999 −9999 −9999 −9999 −9999 −9999−9999 −9999 −9999 −9999 −9999 −9999 −9999 1 X−9999 −9999 −9999 −9999−9999 −9999 −9999 − 9999 −9999 −9999 USW00014745201101WT13 1 X 1 X−9999−9999 1 X−9999 −9999 −9999 −9999 −9999 −9999 −9999 −9999 −9999 1 X−9999−9999 −9999 1 X−9999 −9999 −9999 1 X−9999 1 X 1 X 1 X 1 X−9999 1 X−9999USW00014745201101WT16 1 X 1 X−9999 −9999 −9999 −9999 −9999 −9999 −9999−9999 −9999 −9999 −9999 −9999 −9999 −9999 −9999 1 X 1 X−9999 −9999 −9999−9999 −9999 −9999 −9999 −9999 −9999 −9999 −9999 −9999USW00014745201101WT17−9999 −9999 −9999 −9999 −9999 −9999 −9999 −9999−9999 −9999 −9999 −9999 −9999 −9999 −9999 −9999 −9999 1 X−9999 −9999−9999 −9999 −9999 −9999 −9999 −9999 −9999 −9999 −9999 −9999 −9999USW00014745201101WT18−9999 −9999 −9999 −9999 1 X−9999 1 X 1 X−9999 −9999−9999 1 X−9999 −9999 1 X−9999 −9999 1 X 1 X 1 X 1 X−9999 1 X−9999 1X 1X1X 1X 1X−9999 −9999 USW00014745201101WT19−9999 −9999 −9999 −9999 −9999−9999 −9999 −9999 −9999 −9999 −9999 −9999 −9999 −9999 −9999 −9999 −9999−9999 1 X−9999 −9999 −9999 −9999 −9999 −9999 −9999 1 X−9999 −9999 −9999−9999 USW00014745201101WT22−9999 −9999 −9999 −9999 −9999 −9999 −9999−9999 −9999 −9999 −9999 1 X−9999 −9999 −9999 −9999 −9999 −9999 −9999−9999 1 X−9999 −9999 −9999 −9999 −9999 1 X−9999 1 X 1 X−9999

The foregoing data set, in Table 4, includes daily (not hourly)cumulative weather data. In each box, which corresponds to one line ofdata in the original files, there is a weather station identifier (e.g.,USW00014745201101) that identifies the weather station, from which thedata in that box originated, followed by encoded data for one month ofweather history. After the weather station identifier, in each box,there is a code that indicates the kind of data represented in that box.A few examples include TMAX (which refers to the maximum temperature forthat day), TMIN (which refers to the minimum temperature for that day),PRCP (which refers to total precipitation for that day), SNOW (whichrefers to total snow fall for that day), SNWD (which refers to snowdepth accumulated on that day), WT01 (which refers to fog, ice fog, orfreezing fog), WT02 (which refers to heavy fog or heaving freezing fog),WT13 (which refers to mist), WT16 (which refers to rain, which mayinclude freezing rain, drizzle, and freezing drizzle), WT17 (whichrefers to freezing rain), WT18 (which refers to snow, snow pellets, snowgrains, or ice crystals), WT19 (which refers to an unknown source ofprecipitation), WT22 (which refers to ice fog or freezing fog), etc.Other GHCN abbreviations (in the table above and otherwise) areavailable online.

In a typical implementation, the data from Table 4, above, can bedeciphered (either by the system 106 or by a user of the system) and therelevant cumulative daily data may be stored (e.g., in a computer-basedmemory in the system 106) for later use.

Next (at 304 b 2), in FIG. 3, the system 106 (e.g., METAR precipitationcalculator 222 b) calculates daily total precipitation and daily totalfreezing precipitation from the collected METAR data.

There are a variety of ways to calculate (at 304 b 2) daily totalprecipitation from the collected METAR data. According to an exemplaryimplementation, the system 106 searches the METAR data that has beenstored in the relational database for any hourly METAR data entries thatindicate (e.g., by the presence of a non-zero entry in thePRECIPPREVHR_MM column of Table 3, and/or by a corresponding one of theICAO designators indicating the presence of precipitation in theprevious hour) that precipitation has occurred in the previous hour. Thesystem 106 then simply adds up the indicated precipitation amounts. Insome implementations, any non-hourly data entries (e.g., 1/19/11, 9:150.51 in Table 3) might be discarded, or otherwise accounted for to notdouble count any precipitation, during this calculation process.

There are a variety of ways to calculate (at 304 b 2) daily totalfreezing precipitation from the collected METAR data. According to anexemplary implementation, the system 106 may perform a text search ofthe METAR data in the relational database to identify data entries withICAO designators that indicate the occurrence of freezing rain (e.g.,FZRA), and then add up the corresponding precipitation values from thePRECIPPREVHR_MM column (see Table 3).

After calculating the daily total precipitation and daily total freezingprecipitation values, the system 106 may store those values in acomputer-based memory. The system 106 may also divide the daily totalfreezing precipitation values by the daily total precipitation values toget the fraction of the daily total precipitation that was freezing andstore these fraction values in a computer-based memory.

Next (at 304 b 3), the system 106 (e.g., GHCN filter and extractor 222c) filters the GHCN data stored in the computer-based memory to identifyany days that have freezing rain entries, and, for each of those days,extracts the daily cumulative precipitation amount.

Thus, according to this exemplary implementation, the system 106acquires, and stores, hourly precipitation/freezing rain data from onesource (METAR), and daily cumulative precipitation data from a differentsource (GHCN).

Next (at 304 b 4), the system 106 (e.g., map-to-grid/best estimatecalculator 222 d) maps the METAR/GHCN data to the grid and calculates abest estimate for daily freezing rain amounts based on the METAR/GHCNdata.

There are a variety of ways that the system 106 might calculate its bestestimate for daily freezing rain amounts (at a particular location)based on the METAR and/or GHCN data. In an exemplary implementation, thesystem 106 has the capability to decide which data from two overlappingdatasets for total accumulated precipitation to utilize based on dataquality, or when to combine the data, e.g., when complementary. Themethodology for choosing one data set over another or whether to combinedata sets can be accomplished in numerous ways.

In an exemplary embodiment, it may be done in a hierarchical fashion. Inthis example, the GHCN and METAR data are mapped to grid cells bymatching each grid cell with any GHCN or METAR station located within acertain distance (e.g., 75 km) of the grid cell centroid. For any gridcell with both GHCN and METAR data available, a best estimate of dailyfreezing rain totals may be calculated using hierarchical rules tocombine the two datasets. In those instances, where available, GHCNdaily total precipitation values (which tend to be more accurate forfull day estimates) may be used, but those values may be prorated by theproportion of the corresponding METAR precipitation that is freezingprecipitation. This approach leverages the higher quality totalprecipitation amounts from the GHCN data with the more temporallyprecise differentiation of type of precipitation from the METARobservations.

In some implementations, the system 106 has fallback processes when anexpected, or potentially useful, data source might be missing. In thisexemplary implementation, rules are defined (and stored by the system106 in computer-based memory) to handle instances where one of the datatypes is not available. When METAR data alone is available (but notcorresponding GHCN data), the METAR daily freezing rain accumulationsare used as-is. When GHCN data alone is available (but not correspondingMETAR data), the daily total precipitation values are used, but only ondays flagged as containing freezing precipitation, and the values may beprorated by the average proportion of freezing precipitation to totalprecipitation from all the METAR data, for example. The results of thisprocessing may be stored, for example, in a temporary computer-basedmemory or storage medium for later use in subsequent calculations.

Table 5, below, shows an example of the type of data that might resultfrom the ingesting and pre-processing of METAR and GHCN station data (at304 b) as described above.

TABLE 5 Combined Processed METAR and GHCN data GHCN Precip GHCN FrzMETAR Precip METAR Frz Frz Precip Grid ID Date (cm) Flag (cm) Fraction(cm) 2285082491 Jan. 19, 2011 0.46 1 0.81 0.26 0.12

This exemplary table includes data that relates to one particular cellin the grid (i.e., the cell that corresponds to GRID ID 2285082491).This grid cell, obviously, would correspond to a particular geographicalregion. The data includes the cell identifier (in the GRID ID column),the date of the corresponding event (in the DATE column), theprecipitation amount for that day as reflected in the GHCN data (in theGHCN PRECIP (CM) column), a flag (1 or 0) to indicate whether theGHCN-based precipitation data in the previous column was freezingprecipitation (1) or not (0) (in the GHCN FRZ FLAG column), the dailyprecipitation amount based on the METAR data (in the METAR PRECIP (CM)column), a fraction of the previous column's precipitation amount thatwas freezing precipitation (in the METAR FRZ FRACTION column), and thesystem's 100 best estimate of freezing precipitation at thecorresponding geographical location for the day based on the METAR andGHCN data.

In the illustrated implementation, the grid cell is the grid cell thatcorresponds to GRID ID 2285082491, the date of the corresponding eventis 1/19/11, the precipitation amount for that day as reflected in theGHCN data is 0.46 cm, the GHCN freezing precipitation flag indicatesthat at least some of the GHCN precipitation was freezing, theprecipitation amount for that day as reflected by the METAR data is 0.81cm, the fraction of the day the METAR precipitation was freezing is0.26, and the system's 100 best estimate of freezing precipitation atthe corresponding geographical location for the day based on the METARand GHCN data is 0.12 cm. In this example, the system 106 may havecalculated this best estimate by multiplying the precipitation amountfor that day as reflected by the METAR data (0.81 cm) by the fraction ofthe day the METAR precipitation was freezing (0.26). In this regard0.81×0.26=0.12.

The system, in a typical implementation, performs these types ofcalculations for each cell in the grid and stores the resulting data ina computer-based memory.

Next, the system 106 (at 306) takes into account, for each respectiveone of the grid cells, the probability that a storm will occur in thegeographical region that corresponds to the grid cell. Consideration inthis regard is given to both the data associated with METAR and GHCN (in306 a), as well as the data associated with CRREL (at 306 b).

More particularly, the system 106 (e.g., the METAR/GHCN probabilityestimator 224, at 306 a) pays consideration to the data associated withMETAR and/or GHCN in accounting for the probability (or likelihood) thata storm (of a particular severity level) will occur in each geographicalregion that corresponds to one of the grid cells by: counting (with theMETAR/GHCN historical occurrence counter 224 a, at 306 a 1) thehistorical occurrences (represented in the METAR and/or GHCN data) ofstorms (having particular severity levels) that affected the geographicregion associated with each respective grid cell, calculating (with theMETAR/GHCN probability calculator 224 b, at 306 a 2) an annualprobability, for each cell, of a storm (with the corresponding severitylevel) affecting the corresponding geographical region based on thehistorical occurrences, and calculating (with the METAR/GHCNseverity-weighted index calculator 226, at 306 a 3) severity-weightedindices for each respective grid cell based, at least in part, on thecalculated probability of a storm affecting the correspondinggeographical region based on the counted historical occurrences.

Likewise, the system 106 (e.g., the CRREL probability estimator 218, at306 b) pays consideration to the data associated with CRREL inaccounting for the probability (or likelihood) that a storm (with aparticular severity level) will occur in each geographical region thatcorresponds to one of the grid cells by: aligning the data with the gridcells and counting (with the CRREL historical occurrence counter 218 a,at 306 b 1) the historical occurrences (represented in the CRREL data)of storms (with that severity level) that affected the geographic regionassociated with each respective grid cell, calculating (with the CRRELprobability calculator 218 b, at 306 b 2) an annual probability, foreach cell, of a storm (with that severity level) affecting thecorresponding geographical region based on the historical occurrences,and calculating (with the CRREL severity-weighted index calculator 220,at 306 b 3) severity-weighted indices for each respective grid cellbased, at least in part, on the calculated probability of a stormaffecting the corresponding geographical region based on the countedhistorical occurrences.

These steps can be performed in a number of different ways. According toone such example, the system 106 calculates probabilities of ice stormsand freezing rain events at various severity levels and by location(e.g., on a grid-by-grid basis).

In this example, for each grid cell, all the ice storms from the CRRELdataset that occurred over a period of time (e.g., between 1950 and2015) and that spatially impact that cell are collected. The system 106then categorizes the ice storms based on the severity indices previouslycalculated for each storm (see, e.g., 304 a 4 in FIG. 3). As an example,the ice storms may be categorized using the following categories forseverity index ranges of: 1.5-3.5, 3.5-4.5, 4.5-5.5, 5.5+. Otherembodiments could use other categories depending on the indicescalculated.

Next, in this exemplary process, the system 106 collects, for each gridcell, all of the freezing rain events from the pre-processed METAR andGHCN ground-station data. These freezing rain events are alsocategorized (e.g., by daily accumulated freezing rain totals, see, e.g.,304 b 4). As an example, the categories may be based on dailyaccumulated freezing rain total ranges of: 0.75-1.0″, 1.0-1.5″,1.5-2.0″, 2.0+″.

After categorization, the system 106 calculates the probability of icestorm and freezing rain events at a particular severity level bycounting the number of events over a time period in that severity level,which typically corresponds with the amount of available data (though itcan be configured otherwise), and fitting a uniform probabilitydistribution. Thus, if the system 106 counts fifty storms (of aparticular severity level) that have affected a particular geographicalregion over the course of one hundred years, then the system 106 mightcalculate an annual probability of a storm (with that severity)affecting that geographical region to be 50% (i.e., 50 occurrences/100years×100=50). Although the system may be configured to assume that auniform distribution of storms per year should apply in calculating theprobability (or likelihood) of a storm occurring in a particular yearand at a particular location, in some implementations, other probabilitydistributions, such as the Poisson distribution, may apply.

In some implementations, after calculating the raw weather perilprobabilities (at 306 a 2 and 306 b 2), the system 106 calculates (at(306 a 3 and 306 b 3) severity-weighted risk indices, by weighting theprobability of more severe events more heavily than the probably of lesssevere events. In an exemplary implementation, the severity-weightedrisk indices may be defined as follows: r=p₁+2*p₂+³*p₃+4*p₄, where r isa severity-weighted risk index, and p_(i) is the probability for aseverity category, in increasing order of severity. In an exemplaryembodiment, the risk indices are calculated independently from the CRRELice storm probabilities and the ground station freezing rainprobabilities. The results of such calculations are illustrated in Table6 and Table 7, below.

TABLE 6 CRREL-derived Risk Index Calculation Grid ID Counts Prob_c1Prob_c2 Prob_c3 Prob_c4 Risk Index 2286888192 3 0.031 0.000 0.015 0.0000.077 2286910489 2 0.015 0.000 0.015 0.000 0.062 2286924415 0 0.0000.000 0.000 0.000 0.000 2286924419 6 0.077 0.015 0.000 0.000 0.1082286975572 0 0.000 0.000 0.000 0.000 0.000 2286975576 12 0.169 0.0150.000 0.000 0.200 2286981621 0 0.000 0.000 0.000 0.000 0.000 22869816361 0.000 0.000 0.015 0.000 0.046 2286992376 2 0.031 0.000 0.000 0.0000.031 2286992344 0 0.000 0.000 0.000 0.000 0.000

TABLE 7 Combined Ground Station Derived Risk Index Grid ID CountsProb_c1 Prob_c2 Prob_c3 Prob_c4 Risk Index Station Hist (wks) 22868881921 0.033 0.000 0.000 0.000 0.033 10600 2286910489 0 0.000 0.000 0.0000.000 0.000 10600 2286924415 4 0.067 0.067 0.000 0.000 0.200 106112286924419 23 0.467 0.233 0.000 0.033 1.067 10628 2286975572 4 0.0670.067 0.000 0.000 0.200 10611 2286975576 23 0.467 0.233 0.000 0.0331.067 10628 2286981621 2286981636 2286992376 1 0.000 0.000 0.000 0.0330.133 10636 2286992344

The information in Table 6 includes cell identifiers (in the GRID IDcolumn), a count of the historical occurrences (in the CRREL data) of astorm affecting the corresponding geographical region (in the COUNTScolumn), a probability of a storm in a first (or lowest) severitycategory occurring in the corresponding geographical region (in thePROB_C1 column), a probability of a storm in a second (slightly higher)severity category occurring in the corresponding geographical region (inthe PROB_C2 column), a probability of a storm in a third (even higher)severity category occurring in the corresponding geographical region (inthe PROB_C3 column), a probability of a storm in a fourth (higher still)severity category occurring in the corresponding geographical region (inthe PROB_C4 column), and the associated severity-weighted risk index forthat grid cell (defined by the: r=p₁+2*p₂+3*p₃+4*p₄ equation).

Grid cell 2286888192 in Table 6 has a severity-weighted risk index of0.77 (based on CRREL data), which the system 106 calculates by using thefollowing approach (based on the foregoing equation):r=0.033+0.015*3=0.077.

The information in Table 7 includes cell identifiers (in the GRID IDcolumn), a count of the historical occurrences (in the METAR and GHCNdata) of a storm affecting the corresponding geographical region (in theCOUNTS column), a probability of a storm in a first (or lowest) severitycategory occurring in the corresponding geographical region (in thePROB_C1 column), a probability of a storm in a second (slightly higher)severity category occurring in the corresponding geographical region (inthe PROB_C2 column), a probability of a storm in a third (even higher)severity category occurring in the corresponding geographical region (inthe PROB_C3 column), a probability of a storm in a fourth (higher still)severity category occurring in the corresponding geographical region (inthe PROB_C4 column), an associated severity-weighted risk index for thatgrid cell (defined by the: r=p₁+2*p₂+3*p₃+4*p₄ equation), and a count ofthe number of weeks of station history that apply.

Grid cell 2286888192 in Table 7 has a severity-weighted risk index of0.033, which the system 106 calculates by using the following approach(based on the foregoing equation): r=0.033.

The system 106 typically stores the severity-based risk indices (and, insome implementations, the other information in Tables 6 and 7) intemporary storage for later processing.

Next (at 308), in the illustrated implementation, the system 106 (e.g.,the blender 228) blends the two estimated severity-weighted indices foreach grid cell.

This blending (at 308) can be performed in a number of ways. In oneexample, the system 106 retrieves all available risk indices fromtemporary storage and then blends them by weighted averaging (e.g., at308 a, 308 b). In this regard, the system 106 may adjust the weights toaccount for the quality of each respective data source. Data quality maybe assessed internally by the system 106 or the system 106 may ingestand rely upon external quality assessments (e.g., as provided by a humanor some other computer-based quality assessment system). In oneexemplary embodiment, the system 106 weights the METAR and/or GHCNground station data more for stations that have longer and more complete(e.g., gap-free) temporal records, because the probability distributionfitting tends to be more accurate with more complete data sets. TheCRREL ice storm data in this regard are weighted uniformly. Whenblending the ground station and CRREL data together, the CRREL data maybe weighted equally to the highest quality ground station, because theCRREL dataset typically undergoes significant manual quality control inits creation.

Table 8, below, illustrates an exemplary outcome of the blending of thetwo severity-risk indices (from Table 6 and 7, above).

TABLE 8 Blending of CRREL and Ground Station Risk Indices Grid IDProb_c1 Prob_c2 Prob_c3 Prob_c4 Risk Index 2286888192 0.032 0.000 0.0060.000 0.051 2286910489 0.006 0.000 0.006 0.000 0.024 2286924415 0.0400.040 0.000 0.000 0.120 2286924419 0.312 0.147 0.000 0.020 0.6452286975572 0.040 0.040 0.000 0.000 0.120 2286975576 0.348 0.147 0.0000.020 0.682 2286981621 0.000 0.000 0.000 0.000 0.000 2286981636 0.0000.000 0.015 0.000 0.046 2286992376 0.012 0.000 0.000 0.020 0.0932286992344 0.000 0.000 0.000 0.000 0.000

The data in Table 8 includes cell identifiers (in the GRID ID column, aweighted and blended probability of a storm in a first (or lowest)severity category occurring in the corresponding geographical region (inthe PROB_C1 column), a weighted and blended probability of a storm in asecond (slightly higher) severity category occurring in thecorresponding geographical region (in the PROB_C2 column), a weightedand blended probability of a storm in a third (even higher) severitycategory occurring in the corresponding geographical region (in thePROB_C3 column), a weighted and blended probability of a storm in afourth (higher still) severity category occurring in the correspondinggeographical region (in the PROB_C4 column), and the weighted andblended severity-weighted risk index for that grid cell. The blendedseverity-weighted risk index values are calculated by weighting andblending the risk index values calculated from the CRREL and groundstation probabilities (e.g., not by reapplying the risk index formula tothe weighted and blended probability values).

Grid cell 2286888192 in Table 8 has a severity-weighted risk index of0.051, which the system 106 calculates by a weighted average of the riskindex values for grid cell 2286888192 from Tables 6 and 7 (0.077 and0.033 respectively).

Next (at 310), the system 106 (e.g., spatial smoother 230) applies aspatial smoothing process to the blended severity-weighted risk indicesacross the grid.

This spatial smoothing can be performed in a number of different ways.In one exemplary implementation, the system 106 applies spatialsmoothing by re-calculating the risk value (i.e., the severity-weightedrisk index) for a given grid cell as a statistical average of the riskvalue for that cell and the risk values of all of the grid cells withina certain distance (or in contact with) of the given cell. Thissmoothing process tends to reduce spatial differentiation of riskindices across short geographical distances, where geophysical sourcesof different risk levels are not expected to exist from a meteorologicalperspective.

The distance scale of the smoothing can be tuned separately in thelatitudinal direction and in the longitudinal direction. Due to therelatively small sample size of ice storms, and their shapes in theCRREL dataset, the unsmoothed risk maps may have features that areartificially elongated in the longitudinal direction and narrow in thelatitudinal direction. This can be corrected for by applying a longersmoothing scale in the latitudinal direction than in the longitudinaldirection. For example, as indicated in FIG. 3 (at 310 a), the smoothingscale may be up to 30% (e.g., 10%-30%) longer in the north-southdirection than in the east-west direction. In one exemplary embodiment,the latitudinal smoothing scale is 260 km while the longitudinal scaleis 200 km. The system 106, in these implementations, smoothesseverity-related probability results and varies the smoothing scaledependent on direction.

In a typical implementation, the system 106 stores the smoothed,severity-weighted indices in a computer-based memory (e.g., in arelational database).

Next (at 312), after smoothing, the system 106 (e.g., binner 231)categorizes each risk index into a risk zone. This categorization can beperformed in a number of different ways. According to one exemplaryimplementation, once smoothed, severity-weighted risk indices arecalculated for all the cells in a given geographic area, the system 106retrieves the indices from the relational database on which they arestored. The system 106 then groups or bins the indices to provide arelative ranking of risk for the various cells. Binning may beaccomplished in a variety of different ways. In one exemplaryembodiment, the system 106 groups the indices into bins numbered 1through 10, though in other embodiments, indices could be group intosmaller or larger numbers of bins and/or use other descriptors for thebins, such as color codes. In this embodiment, data is binned such thatan equal amount of data is grouped into each bin. In other embodiments,the risk indices could be grouped based on differences from a mean riskindex or by equal intervals between the lowest and highest valued riskindices. Other types of binning or categorization are possible as well.

In a typical implementation, the system 106 stores the scores of eachcell (that represents the categorization of that cell) in a relationaldatabase for later extraction and/or analysis and/or distribution to endusers.

In a typical implementation, the system 106 is able to output data (viadata distribution/output module 232) to a system user at any one or moreof the network's 100 computer-based user terminals 114. This output datacan be configured in any one of a variety of different ways to provideconvenience to the user.

FIG. 4 shows an exemplary screenshot that the system 106 might produceor provide access to at one or more of the network's 100 computer-baseduser terminals 114.

The illustrated screenshot shows a map of the contiguous United Statescolor-coded to show the various risk categories that each geographicalregion represented in the map falls within. There is a risk score legendthat explains the color-coding scheme near the bottom of the screenshot.

In some implementations, if a user hovers a cursor over some region onthe geographical map on the screen, the system's risk score for thatregion will appear. In the illustrated example, the cursor is hoveringover the “score 7” message.

In some implementations, double clicking on some region of the map willcause the display to zoom in on the double clicked region and may alsoprovide additional information/insight about the associated risk score,region and underlying data.

FIG. 5 shows another exemplary screenshot that the system 106 mightproduce or provide access to at one or more of the network's 100computer-based user terminals 114.

The screenshot in FIG. 5 shows a zoomed in aerial view map of aparticular geographical area of the United States (part of Dallas, Tex.to be precise). The aerial view map is color coded based on risk score,and there is a risk score legend to explain the color coding on the map.

The screenshot identifies an annual ice storm probability (7.28%) thatthe system 106 calculated for a grid cell corresponding to thegeographical region shown in the screenshot. The screenshot alsoidentifies a count of historical ice storm occurrences (5) that affectedthe grid cell/geographical region, as determined by the system 106. Thescreenshot also shows a bar graph indicating the distribution ofhistorical ice storm activity for the grid cell/geographical region bymonth, as determined and produced by the system 106. The screenshotshows a second graph indicating the distribution of historical ice stormactivity for the grid cell/geographical region by severity, asdetermined and produced by the system 106.

A number of embodiments of the invention have been described.Nevertheless, it will be understood that various modifications may bemade without departing from the spirit and scope of the invention.

For example, the various modules or components (e.g., of system 106)disclosed herein can be implemented as virtually any kind ofcomputer-based modules and/or components (e.g., implemented in hardware,or in hardware executing appropriate software).

Data from sources outside the system 106 can be received and processed(or preprocessed) in any number of a variety of different ways. Thespecific risk calculations can be modified as well. The probabilitycalculations also can be modified. The mapping and grid generation canbe modified. The blending process and spatial smoothing can be modified.The data output can be modified.

Generally speaking, in the implementations disclosed herein, after gridcreation, the system 106 collects and processes data sets to create riskscores. The data sets may vary by the embodiment, but generally involvesome numerical measure of severity, whether measured by ice accretion,accumulation of freezing rain, wind, or a combination of these, as wellas subjective, qualitative, human descriptors of the storm. Thecollection, and data formatting, methods may vary depending on the typesand/or sources of data that will be ingested. Data sets may be storedlocally or remotely. Collection from remote data storage may be made inone of many ways, including through any manner of electronic connection,such as the Internet, or a dedicated data connection. Collection may bemade at different times and in different manners. In a simpleembodiment, a human-directed ad hoc collection process could manuallycollect selected data through electronic communication devices orsystems, such as the Internet. In another embodiment, collection couldtake place at pre-determined time intervals (configured, e.g., tocoincide with expected data releases or to take place when likelihood ofdata congestion in the connection is remote). In other embodiments,collection could take place once the system 106 receives an indicationthat an icing event has taken place, ensuring that new data is present.In all of these embodiments, the collection process could includecomponents to screen out already gathered and/or duplicative data. Oneembodiment of such functionality would involve a mechanism for comparingthe file size of available data releases to previously gatheredreleases. Yet another embodiment could evaluate and screen out datareleases with a zero-file size, indicating no new data.

Data collection is generally different for all three data sourcesmentioned herein (CRREL, METAR, and GHCN). For the CRREL Database, oneembodiment involves manually downloading over the Internet an ice stormdatabase hosted on a United States Army server in Geospatial InformationSystem (GIS) shapefile format. The METAR and GHCN data may be retrievedand processed differently. The raw METAR data may be downloaded fromNOAA's servers and loaded into a PostgeSQL database for later use. Inone embodiment, each hour a data retrieval and transformation processdownloads METAR data from a file transfer protocol (FTP) server hostedby the NOAA Meteorological Assimilation Data Ingestion System (MADIS).Once new data becomes available on the server, the process collects allavailable METAR observations for a respective hour via a single netCDFdata file that is downloaded. This file is then opened, parsed, andtransformed into comma-separated format (CSV) for storage in arelational database. The processing of the available METAR data withinthe single file may employ an open-source NOAA-created suite ofLinux/Unix commands called MADIS-API. This set of tools enables theextraction and manipulation of METAR data for a respective hour into theformats and standards expected both for storage in a database as well asfor use with downstream processes described herein. Other embodiments ofthe ingest process could utilize bulk download techniques, in additionto ancillary external data sources to obtain the most continuous, highquality METAR dataset possible for the processes herein.

Additionally, raw GHCN data is downloaded from a NOAA server and storedin the original file format on a Linux server. In some implementations,the system 106 essentially replicates a NOAA GHCN HTTP data serverlocally on hard drive storage twice a day using an open-sourceLinux/Unix command line utility. This automated syncing process ensuresthat the latest GHCN data is available on local storage by iterativelycomparing the locally stored data to the data available on the NOAA GHCNserver. If a specific file on the FTP server is deemed to be updated(whether by a differing file size, modification timestamp, or othermetric), the version of the file on the FTP server is downloaded andstored on local storage, replacing the old local file version. Furtherembodiments could include other bulk retrieval techniques or techniquestargeted at specifically downloading specific files from a server ratherthan full replication.

In a typical implementation, the system 106 has the ability to gatherand homogenize different data sources and severity indicators.Therefore, the collection process typically includes standardization ofingested data so that it can be compiled and readily analyzed forcalculation of a risk score. The system 106 therefore typically hasfunctionality to alter the ingested data to fit a predetermined metadatascheme so that different data sets can be compared, combined, orwhatever manipulation is necessary for later downstream processes. Themethod of alteration or standardization will differ depending on thedata set and user requirements. In an exemplary embodiment, the system106 may remove duplicated data and unnecessary fields from the raw CRRELdata and add metadata fields for later use in assessing severity, suchas ice thickness and subjective severity ratings.

Other homogenization techniques such as combining similar data sourcesunder a single table or schema may be utilized as well. For instance, inan exemplary embodiment, the system 106 may process the raw METAR andGHCN data eliminating unnecessary metadata fields and combining into asingle table for later input into the system's risk scoringcomponents/functionalities.

The system's 106 risk scoring methodology may use different severityindicators for historical storms. An important severity indicator inthis regard is subjective reporting of the event's impact on theaffected community. The system 106, in a typical implementation, gathersand collects such severity indicators through scraping and text parsingtechniques. The source of such subjective descriptors can be a gathereddatabase of such information. For instance, in an exemplary embodiment,the system 106 gathers textual descriptions of particular ice storms onthe CCREL server in HTML files associated with identified storms. Usingtextual searching and parsing techniques the system 106 identifies wherereported ice accretion amounts are contained in the text and convertsthe text into numerical values, and identifies and quantifies subjectivedescriptors of the storm, such as the word “worst.” The system 106 canalso, in various implementations, calculate severity based upon stormsize, so the system 106 has the ability to calculate size from ingesteddata, whether loose, textual descriptions of how many states areaffected to, as in the current embodiment, GIS data, which is containedin the CRREL database. The system 106 thus typically gathers bothsubjective severity information as well as numerical information usingscraping and parsing techniques. This can be done is a variety ofdifferent ways.

Severity reports may also consist of reports by observers distributedover a series of connected devices, such as the Internet or social mediaplatforms. Some embodiments of the system 106 may have the ability tosearch for textual descriptions of storms and events on such connecteddevices or remotely hosted sites, such as HTML or other types ofweb-pages or social media platforms, to gather ice amount information,subjective descriptors, and/or other indications of severity (such asnumbers of people losing electricity and spatial area of the storm).Broader searching for severity information may be commenced uponverification by the system 106 that freezing participation has occurred,which, in one embodiment, could be signaled by the existence of reportsof freezing precipitation, such as from GHCN or in the METAR data.Utilizing the time and location associated with the ground stationinformation, the system 106 may be configured to target appropriatesources for web-crawling and other searching methodologies and then usetextual parsing to extract severity-related information.

The system also generally ingests freezing precipitation amount data andseparately assesses severity based on that amount. The freezingprecipitation data can, in some implementations, provide an independentmeasure of severe storms that, while not capturing ice accretion amountsmay be more comprehensive than the data available in the ice stormdatabase for example. In certain embodiments, freezing precipitationamounts may be gathered from the METAR and GHCN data sets. The system106 may have the capability of calculating total precipitation amountsfrom a storm even if the collected data sets do not have such a field.For instance, for each METAR station, the “Present Weather” text fieldprovided in the METAR records may be used to identify which, if any,hours of each day had freezing precipitation. The observed precipitationaccumulations from these hours may be summed to get the daily freezingrain accumulation totals. This, too, may be done in a variety ofdifferent ways.

The relational database, and computer-based memory, disclosed ormentioned herein can take on any number of possible formats. In ageneral sense, a relational database may include, for example, datastored in a manner to recognize relationships among the stored data. Inone exemplary implementation, a relational database may organize datainto one or more tables (or “relations”) of columns and rows, with aunique key identifying each row, for example. Generally, eachtable/relation may represent one type of entity or element. The rows mayrepresent instances of that type of entity or element and the columnsrepresenting values attributed to that instance. One example of arelational database is a PostgreSQL database. The relational database,of course, may be accompanied by any requisite supporting systems, suchas relational database management systems (RDBMS), which typicallyutilize Structured Query Language (SQL) as the language for querying andmaintaining the database.

An exemplary configuration of modules or components within the system106 is disclosed, for example, in FIG. 2 herein. In someimplementations, two or more of the disclosed modules or components (ortheir associated functionalities) may be combined into one single moduleor component, and/or any one module or component (or its associatedfunctionalities) can be split into two or more modules or components.Moreover, in some implementations, some (e.g., one or more) of themodules or components may be eliminated. In some implementations, one ormore of the connections between the modules or components in system 106may be modified, added to, and/or dispensed with.

The system 106, and its associated functionalities, may be useful to awide variety of individuals and/or organizations. One such exemplaryuser-base would be governmental agencies and non-governmental agenciesthat are charged with the responsibilities of predicting, publicizingand responding to the effects of ice storms and/or freezing rain events.Additionally, insurer and re-insurers might benefit from the improvedrisk assessment capabilities that the system 106 embodies.

The collector components (and scrapers) can contain one or morecollector instances to collect data from the storm forecast distributionrepositories (multiple agencies).

The system 106 may receive input information from any one or more of avariety of different sources, including those specifically mentionedherein, as well as others. Additionally, the system 106 may beconfigured to output (or make available to users) the data it produces,and make that data easy for a user to leverage, in a variety ofdifferent ways. The system 106 may be configured to take into accountone or more (or all) of the weather-related data mentioned herein, aswell as, perhaps, other weather-related data that may not have beenexplicitly mentioned herein.

The word “best” as used, for example, in the phrase “best estimate,” forexample, generally refers to the idea of being very good. To be clear,“best,” in this regard, does not require absolute perfection. Best canmean, for example, high accuracy and usefulness.

In various embodiments, the subject matter disclosed herein can beimplemented in digital electronic circuitry, or in computer-basedsoftware, firmware, or hardware, including the structures disclosed inthis specification and/or their structural equivalents, and/or incombinations thereof. In some embodiments, the subject matter disclosedherein can be implemented in one or more computer programs, that is, oneor more modules of computer program instructions, encoded on computerstorage medium for execution by, or to control (or controlling) theoperation of, one or more data processing apparatuses (e.g.,processors). Alternatively, or additionally, the program instructionscan be encoded on an artificially generated propagated signal, forexample, a machine-generated electrical, optical, or electromagneticsignal that is generated to encode information for transmission tosuitable receiver apparatus for execution by a data processingapparatus. A computer storage medium can be, or can be included within,a computer-readable storage device, a computer-readable storagesubstrate, a random or serial access memory array or device, or acombination thereof. While a computer storage medium should not beconsidered to include a propagated signal, a computer storage medium maybe a source or destination of computer program instructions encoded inan artificially generated propagated signal. The computer storage mediumcan also be, or be included in, one or more separate physical componentsor media, for example, multiple CDs, computer disks, and/or otherstorage devices.

Some of the operations described in this specification can beimplemented as operations performed by a data processing apparatus(e.g., one or more processors) on data stored on one or morecomputer-readable storage devices or received from other sources.

Unless otherwise indicated, the term “processor,” as used herein, canrefer to one or more computer-based processors or processing units.Moreover, a “processor,” or “processing unit” encompasses all kinds ofapparatus, devices, and machines for processing data, including by wayof example a programmable processor, a computer, a system on a chip, ormultiple ones, or combinations, of the foregoing. The apparatus caninclude special purpose logic circuitry, e.g., an FPGA (fieldprogrammable gate array) or an ASIC (application specific integratedcircuit). The apparatus can also include, in addition to hardware, codethat creates an execution environment for the computer program inquestion, for example, code that constitutes processor firmware, aprotocol stack, a database management system, an operating system, across-platform runtime environment, a virtual machine, or a combinationof one or more of them. The apparatus and execution environment canrealize various different computing model infrastructures, such as webservices, distributed computing and grid computing infrastructures.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinventions disclosed herein, but rather as descriptions of featuresspecific to particular embodiments of particular inventions. Certainfeatures that are described in this specification in the context ofseparate embodiments can also be implemented in combination in a singleembodiment. Conversely, various features that are described in thecontext of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations, one or more features from a described combination can insome cases be excised from the combination, and certain featuresdisclosed may be combined into different subcombinations or variationsthereof.

Similarly, while operations are depicted in the drawings and describedherein as occurring in a particular order, this should not be understoodas requiring that such operations be performed in the particular ordershown or in sequential order, or that all illustrated operations beperformed, to achieve desirable results. In certain circumstances,multitasking and parallel processing may be advantageous. Moreover, theseparation of various system components in the embodiments describedabove should not be understood as requiring such separation in allembodiments, and it should be understood that the described programcomponents and systems can generally be integrated together in a singlesoftware product or packaged into multiple software products.

In various implementations, the functionalities disclosed herein and/orassociated with the systems and technologies disclosed herein can beaccessed from virtually any kind of electronic computing device(s),including, for example, desktop computers, laptops computers, smartphones, tablet, etc.

Any storage media referred to herein can be or include virtually anykind of media such as electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system (or apparatus or device) orpropagation media. Examples of suitable computer-readable media includesemiconductor or solid-state memory, magnetic tape, removable computerdiskettes, random access memory (RAM), read-only memory (ROM), rigidmagnetic disks and/or optical disks.

In some implementations, certain functionalities described herein (e.g.,those accessible from a computer-based user terminal) may be provided bya downloadable software application (i.e., an app) or in associationwith a website. Furthermore, some of the concepts disclosed herein cantake the form of a computer program product accessible from acomputer-usable or computer-readable medium providing program code foruse by or in connection with a computer or any instruction executionsystem. For the purposes of this description, a computer-usable orcomputer readable medium can be any tangible apparatus that can contain,store, communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, or device.

The risk calculation system is referred to herein as being automated. Ina typical implementation, this means that the computer system itselfperforms at least some (typically many) of the processes disclosedherein without continual involvement or prompting from a human user.

The phrase computer-readable medium or computer-readable storage mediumis intended to include at least all mediums that are eligible for patentprotection, including, for example, non-transitory storage, and, in someinstances, to specifically exclude all mediums that are non-statutory innature to the extent that the exclusion is necessary for a claim thatincludes the computer-readable (storage) medium to be valid. Some or allof these computer-readable storage media can be non-transitory.

Other implementations are within the scope of the claims.

What is claimed is:
 1. A computer-based method of identifying ice stormrisk across a geographical extent, the method comprising: receiving, ata computer-based ice storm risk calculation system, historical dataregarding a plurality of past ice storms, wherein the historical datacomprises, for each respective one of the plurality of past ice storms,data that indicates: a size of a geographical region that was impactedby the ice storm; a thickness of accumulated ice that resulted from theice storm; and qualitative data reflecting human observations of the icestorm's impact; calculating an ice storm severity index based, in part,on the size of the geographical region that was impacted by the icestorm and the thickness of the accumulated ice that resulted from theice storm; and validating the calculated ice storm index with thequalitative data reflecting the human observations of the ice storm'simpact.
 2. The computer-based method of claim 1, further comprising:searching the qualitative data reflecting human observations of the icestorm's impact for one or more specific words or phrases that suggest aparticular level of severity associated with the ice storm.
 3. Thecomputer-based method of claim 2, wherein the qualitative datareflecting human observations of the ice storm's impact comprises one ormore published articles, written by humans, describing the ice storm,the method further comprising: counting a number of times that the oneor more specific words or phrases appear in the one or more publishedarticles, and/or counting how many of the one or more published articlesinclude any of the one or more specific words or phrases.
 4. Thecomputer-based method of claim 3, wherein searching the qualitative datareflecting human observations of the ice storm's impact for one or morespecific words or phrases that suggest a particular level of severityassociated with the ice storm comprises searching the one or morepublished articles for the word “worst.”
 5. The computer-based method ofclaim 2, wherein validating the calculated ice storm severity index withthe qualitative data reflecting the human observations of the icestorm's impact comprises: checking a correlation between the calculatedice storm severity index and occurrences of the one or more specificwords or phrases that suggest a particular level of severity associatedwith the ice storm in the qualitative data.
 6. The computer-based methodof claim 1, wherein the ice storm severity index for the ice storm iscalculated based on: a ratio of the size of the geographical region thatwas impacted by the ice storm versus an average size of geographicalregions that were impacted by a plurality of ice storms, and/or a ratioof the thickness of the accumulated ice that resulted from the ice stormversus an average thickness of accumulated ice that resulted from theplurality of ice storms.
 7. The computer-based method of claim 6,wherein the ice storm severity index is calculated based on thisformula: I i I ^ + A i , where: I_(i) is an ice thickness associatedwith the ice storm, Î is an average ice thickness for the plurality ofthe storms, A_(i) is a spatial area representing the size of thegeographical region that was impacted by the ice storm, and A is anaverage spatial area representing the size of the geographical regionthat was impacted by the plurality of ice storms.
 8. The computer-basedmethod of claim 1, further comprising: superimposing a grid that definesa plurality of grid cells over a geographical map, wherein eachrespective grid cell represents a particular geographical region of themap; calculating an ice storm severity index for each respective one ofthe grid cells.
 9. The computer-based method of claim 1, whereinreceiving, at the computer-based ice storm risk calculation system,historical data regarding the plurality of past ice storms comprises:downloading from a remote computer-based database of damaging ice stormdata, a shapefile associated with the ice storm, other quantitativeinformation about the storm, and scraping one or more hypertext markuplanguage (“HTML”) descriptions of published articles.
 10. Thecomputer-based method of claim 1, further comprising: receiving, at thecomputer-based ice storm risk calculation system, historical dataincluding amounts of precipitation at particular times and locations.11. The computer-based method of claim 10, wherein the historical dataregarding the plurality of past ice storms is downloaded and/or scrapedfrom a first data source, and wherein the historical data includingamounts of precipitation at particular times and locations is downloadedfrom one or more other data sources different than the first datasource.
 12. The computer-based method of claim 11, wherein thehistorical data including amounts of precipitation at particular timesand locations comprises: data from a first precipitation data sourcethat includes daily accumulated freezing rain totals; and data from asecond precipitation data source that includes more frequent indicationsof freezing rain amounts, wherein the method comprising identifying abest estimate of daily freezing precipitation based on the data from thefirst precipitation data source and the second precipitation datasource, based on a set of rules stored in computer-based memory.
 13. Thecomputer-based method of claim 1, further comprising: superimposing agrid that defines a plurality of grid cells over a geographical map,wherein each respective grid cell represents a particular geographicalregion of the map; and for each respective one of the plurality of gridcells: calculating a probability that a subsequent ice storm will affecta corresponding geographical region based on the historical data aboutthe plurality of ice storms; and calculating a probability that asubsequent ice storm will affect a corresponding geographical regionbased on weather-related data from one or more data sources other thanthe source that provided the historical data about the plurality of icestorms.
 14. The computer-based method of claim 13, further comprising:for each respective one of the plurality of grid cells, calculating twoseverity-weighted risk indices (r), each taking into account one of thecalculated probabilities.
 15. The computer-based method of claim 14,wherein each of the severity-weighted risk indices (r) is calculatedbased on:r=p ₁+(2*p ₂)+ . . . (N*p _(n)), where each p is one of the calculatedprobabilities (in order of increasing severity).
 16. The computer-basedmethod of claim 14, further comprising: for each respective one of theplurality of grid cells, blending the two severity-weighted risk indices(r) to produce a single blended risk index for each cell.
 17. Thecomputer-based method of claim 16, further comprising: applying aspatial smoothing process on the blended risk indexes across theplurality of grid cells to produce a smoothed, blended risk index foreach cell.
 18. The computer-based method of claim 17, furthercomprising: categorizing each respective grid cell into one of aplurality of different risk levels based on the smoothed, blended riskindex for that cell.
 19. The computer-based method of claim 18, furthercomprising: providing access, at a computer-based user terminal, to agridded geographic map with a plurality of grid cells, wherein eachrespective grid cell has a corresponding risk level that is viewable oraccessible from the computer-based user terminal.
 20. A computer systemcomprising: a plurality of data sources; a computer-based ice storm riskcalculation system; and a plurality of computer-based user terminals,wherein the plurality of data sources, the computer-based ice storm riskcalculation system, and the plurality of computer-based user terminalsare coupled to one another for communication via a network, wherein thecomputer-based ice storm risk calculation system comprises: acomputer-based processor; and a computer-based memory coupled to thecomputer-based processor, wherein the computer-based processor isconfigured to execute instructions stored in the computer-based memoryto perform the steps comprising: receiving historical data regarding aplurality of past ice storms from a first one of the data sources,wherein the historical data comprises, for each respective one of theplurality of past ice storms, data that indicates: a size of ageographical region that was impacted by the ice storm; a thickness ofaccumulated ice that resulted from the ice storm; and qualitative datareflecting human observations of the ice storm's impact; calculating anice storm severity index based, in part, on the size of the geographicalregion that was impacted by the ice storm and the thickness of theaccumulated ice that resulted from the ice storm; and validating thecalculated ice storm index with the qualitative data reflecting thehuman observations of the ice storm's impact, wherein the computersystem is configured to make available at one or more of thecomputer-based user terminals a gridded geographic map with a pluralityof grid cells, wherein each respective grid cell has a correspondingrisk level that is viewable or accessible from the computer-based userterminal.